B. Dong, Shuxiang Wang, Fan Zhou, Yan Li, Shenquan Wang, Keping Liu, Yuan-chun Li
{"title":"基于临界标识结构的模块化机器人分散鲁棒最优控制","authors":"B. Dong, Shuxiang Wang, Fan Zhou, Yan Li, Shenquan Wang, Keping Liu, Yuan-chun Li","doi":"10.1109/ICIST.2018.8426140","DOIUrl":null,"url":null,"abstract":"This paper presents a decentralized robust optimal control method for modular robot manipulators (MRMs) via a novel critic-identifier (CI) structure-based adaptive dynamic programming (ADP) scheme. The robust control problem of MRMs is transformed into an optimal compensation control approach, which combines model-based compensation control, identifier-based learning control and ADP-based optimal control. The dynamic model of MRMs is formulated based on a torque sensing technique that is deployed for each joint module, where the local dynamic information is utilized effectively to design the model compensation controller. A neural network (NN) identifier is established to approximate the dynamics of the interconnected dynamic coupling (IDC). Based on the ADP algorithm, the Hamiltonian-Jacobi-Bellman (HJB) equation can be solved by constructing a critic NN, and the approximate optimal control policy is derived. The closed-loop robotic system is guaranteed to be asymptotic stable by the implementation of a set of decentralized control policies that have been developed. Finally, simulations verify the effectiveness of the proposed method.","PeriodicalId":331555,"journal":{"name":"2018 Eighth International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Critic-Identifier Structure-Based ADP for Decentralized Robust Optimal Control of Modular Robot Manipulators\",\"authors\":\"B. Dong, Shuxiang Wang, Fan Zhou, Yan Li, Shenquan Wang, Keping Liu, Yuan-chun Li\",\"doi\":\"10.1109/ICIST.2018.8426140\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a decentralized robust optimal control method for modular robot manipulators (MRMs) via a novel critic-identifier (CI) structure-based adaptive dynamic programming (ADP) scheme. The robust control problem of MRMs is transformed into an optimal compensation control approach, which combines model-based compensation control, identifier-based learning control and ADP-based optimal control. The dynamic model of MRMs is formulated based on a torque sensing technique that is deployed for each joint module, where the local dynamic information is utilized effectively to design the model compensation controller. A neural network (NN) identifier is established to approximate the dynamics of the interconnected dynamic coupling (IDC). Based on the ADP algorithm, the Hamiltonian-Jacobi-Bellman (HJB) equation can be solved by constructing a critic NN, and the approximate optimal control policy is derived. The closed-loop robotic system is guaranteed to be asymptotic stable by the implementation of a set of decentralized control policies that have been developed. Finally, simulations verify the effectiveness of the proposed method.\",\"PeriodicalId\":331555,\"journal\":{\"name\":\"2018 Eighth International Conference on Information Science and Technology (ICIST)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Eighth International Conference on Information Science and Technology (ICIST)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIST.2018.8426140\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Eighth International Conference on Information Science and Technology (ICIST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIST.2018.8426140","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Critic-Identifier Structure-Based ADP for Decentralized Robust Optimal Control of Modular Robot Manipulators
This paper presents a decentralized robust optimal control method for modular robot manipulators (MRMs) via a novel critic-identifier (CI) structure-based adaptive dynamic programming (ADP) scheme. The robust control problem of MRMs is transformed into an optimal compensation control approach, which combines model-based compensation control, identifier-based learning control and ADP-based optimal control. The dynamic model of MRMs is formulated based on a torque sensing technique that is deployed for each joint module, where the local dynamic information is utilized effectively to design the model compensation controller. A neural network (NN) identifier is established to approximate the dynamics of the interconnected dynamic coupling (IDC). Based on the ADP algorithm, the Hamiltonian-Jacobi-Bellman (HJB) equation can be solved by constructing a critic NN, and the approximate optimal control policy is derived. The closed-loop robotic system is guaranteed to be asymptotic stable by the implementation of a set of decentralized control policies that have been developed. Finally, simulations verify the effectiveness of the proposed method.